基于多头注意力时空图卷积网络的交通事故预测

doi:10.12066/j.issn.1007-2861.2676

Abstract

Abstract: This paper proposed a novel spatio-temporal graph convolutional network (STGCN) enhanced with a multi-head attention (MHA) mechanism and adaptive adjacency matrices. The MHA mechanism dynamically weighted spatio-temporal features and external environmental factors, while the adaptive adjacency matrices adjusted the connection weights of road networks to improve the model's ability to capture spatial dependencies. Experimental results demonstrated that the model outperformed state-of-the-art (SOTA) models on the London road network dataset, achieving significant improvements across multiple evaluation metrics.

Key words: traffic accident prediction, spatio-temporal graph convolutional network (STGCN), multi-head attention (MHA) mechanism, adaptive adjacency matrix

CLC Number:

TP183

JIANG Tianhao, WANG Rui. Traffic accident prediction based on multi-head attention spatio-temporal graph convolutional network[J]. Journal of Shanghai University（Natural Science Edition）, 2025, 31(4): 678-690.

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Traffic accident prediction based on multi-head attention spatio-temporal graph convolutional network

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